Neural \'{E}tendue Expander for Ultra-Wide-Angle High-Fidelity Holographic Display

Holographic displays can generate light fields by dynamically modulating the wavefront of a coherent beam of light using a spatial light modulator, promising rich virtual and augmented reality applications. However, the limited spatial resolution of existing dynamic spatial light modulators imposes a tight bound on the diffraction angle. As a result, modern holographic displays possess low \'{e}tendue, which is the product of the display area and the maximum solid angle of diffracted light. The low \'{e}tendue forces a sacrifice of either the field-of-view (FOV) or the display size. In this work, we lift this limitation by presenting neural \'{e}tendue expanders. This new breed of optical elements, which is learned from a natural image dataset, enables higher diffraction angles for ultra-wide FOV while maintaining both a compact form factor and the fidelity of displayed contents to human viewers. With neural \'{e}tendue expanders, we experimentally achieve 64$\times$ \'{e}tendue expansion of natural images in full color, expanding the FOV by an order of magnitude horizontally and vertically, with high-fidelity reconstruction quality (measured in PSNR) over 29 dB on retinal-resolution images

Waveguide Holography: Towards True 3D Holographic Glasses

We present a novel near-eye display concept which consists of a waveguide combiner, a spatial light modulator, and a laser light source. The proposed system can display true 3D holographic images through see-through pupil-replicating waveguide combiner as well as providing a large eye-box. By modeling the coherent light interaction inside of the waveguide combiner, we demonstrate that the output wavefront from the waveguide can be controlled by modulating the wavefront of input light using a spatial light modulator. This new possibility allows combining a holographic display, which is considered as the ultimate 3D display technology, with the state-of-the-art pupil replicating waveguides, enabling the path towards true 3D holographic augmented reality glasses

Multisource Holography

Holographic displays promise several benefits including high quality 3D imagery, accurate accommodation cues, and compact form-factors. However, holography relies on coherent illumination which can create undesirable speckle noise in the final image. Although smooth phase holograms can be speckle-free, their non-uniform eyebox makes them impractical, and speckle mitigation with partially coherent sources also reduces resolution. Averaging sequential frames for speckle reduction requires high speed modulators and consumes temporal bandwidth that may be needed elsewhere in the system. In this work, we propose multisource holography, a novel architecture that uses an array of sources to suppress speckle in a single frame without sacrificing resolution. By using two spatial light modulators, arranged sequentially, each source in the array can be controlled almost independently to create a version of the target content with different speckle. Speckle is then suppressed when the contributions from the multiple sources are averaged at the image plane. We introduce an algorithm to calculate multisource holograms, analyze the design space, and demonstrate up to a 10 dB increase in peak signal-to-noise ratio compared to an equivalent single source system. Finally, we validate the concept with a benchtop experimental prototype by producing both 2D images and focal stacks with natural defocus cues.Comment: 14 pages, 9 figures, to be published in SIGGRAPH Asia 202

Acoustic Hologram Optimisation Using Automatic Differentiation

Acoustic holograms are the keystone of modern acoustics. It encodes three-dimensional acoustic fields in two dimensions, and its quality determine the performance of acoustic systems. Optimisation methods that control only the phase of an acoustic wave are considered inferior to methods that control both the amplitude and phase of the wave. In this paper, we present Diff-PAT, an acoustic hologram optimisation algorithm with automatic differentiation. We demonstrate that our method achieves superior accuracy than conventional methods. The performance of Diff-PAT was evaluated by randomly generating 1000 sets of up to 32 control points for single-sided arrays and single-axis arrays. The improved acoustic hologram can be used in wide range of applications of PATs without introducing any changes to existing systems that control the PATs. In addition, we applied Diff-PAT to acoustic metamaterial and achieved an >8 dB increase in the peak noise-to-signal ratio of acoustic hologram.Comment: 25 pages, 5 figures, manuscrip

Depolarized Holography with Polarization-multiplexing Metasurface

The evolution of computer-generated holography (CGH) algorithms has prompted significant improvements in the performances of holographic displays. Nonetheless, they start to encounter a limited degree of freedom in CGH optimization and physical constraints stemming from the coherent nature of holograms. To surpass the physical limitations, we consider polarization as a new degree of freedom by utilizing a novel optical platform called metasurface. Polarization-multiplexing metasurfaces enable incoherent-like behavior in holographic displays due to the mutual incoherence of orthogonal polarization states. We leverage this unique characteristic of a metasurface by integrating it into a holographic display and exploiting polarization diversity to bring an additional degree of freedom for CGH algorithms. To minimize the speckle noise while maximizing the image quality, we devise a fully differentiable optimization pipeline by taking into account the metasurface proxy model, thereby jointly optimizing spatial light modulator phase patterns and geometric parameters of metasurface nanostructures. We evaluate the metasurface-enabled depolarized holography through simulations and experiments, demonstrating its ability to reduce speckle noise and enhance image quality.Comment: 15 pages, 13 figures, to be published in SIGGRAPH Asia 202

Advancing computer-generated holographic display thanks to diffraction model-driven deep nets

Advancements are reported in computer-generated holography proofing RGB 4K display through a new strategy based on diffraction model-driven deep networks. In the new 4K-DMDNet, the network is not a “black box” anymore. Rather, the input-output relation must obey to the physics of wavefront propagation, which is embedded here as a constraint. Thus, a labelled dataset is not required, and the model shows superior generalization capabilities with respect to data-driven approaches. The method is promising for the new generation of RGB 4K holographic display, as well as augmented and virtual reality systems

Automotive Holographic Head-Up Displays.

Funder: Foundation of German BusinessDriver's access to information about navigation and vehicle data through in-car displays and personal devices distract the driver from safe vehicle management. The discrepancy between road safety and infotainment must be addressed to develop safely-operated modern vehicles. Head-up Displays (HUDs) aim to introduce a seamless uptake of visual information for the driver while securely operating a vehicle. HUDs projected on the windshield provide the driver with visual navigation and vehicle data within the comfort of the driver's personal eye box through a customizable extended display space. Windshield HUDs does not require the driver to shift the gaze away from the road to attain road information. This article presents a review of technological advances and future perspectives in holographic HUDs by analyzing the optoelectronics devices and the user experience of the driver. The review elucidates holographic displays and full augmented reality (AR) in 3D with depth perception when projecting the visual information on the road within the driver's gaze. Design factors, functionality and the integration of personalized machine learning (ML) technologies into holographic HUDs are discussed. Application examples of the display technologies regarding road safety and security are presented. An outlook is provided to reflect on display trends and autonomous driving. This article is protected by copyright. All rights reserved

4K-DMDNet: diffraction model-driven network for 4K computer-generated holography

Deep learning offers a novel opportunity to achieve both high-quality and high-speed computer-generated holography (CGH). Current data-driven deep learning algorithms face the challenge that the labeled training datasets limit the training performance and generalization. The model-driven deep learning introduces the diffraction model into the neural network. It eliminates the need for the labeled training dataset and has been extensively applied to hologram generation. However, the existing model-driven deep learning algorithms face the problem of insufficient constraints. In this study, we propose a model-driven neural network capable of high-fidelity 4K computer-generated hologram generation, called 4K Diffraction Model-driven Network (4K-DMDNet). The constraint of the reconstructed images in the frequency domain is strengthened. And a network structure that combines the residual method and sub-pixel convolution method is built, which effectively enhances the fitting ability of the network for inverse problems. The generalization of the 4K-DMDNet is demonstrated with binary, grayscale and 3D images. High-quality full-color optical reconstructions of the 4K holograms have been achieved at the wavelengths of 450 nm, 520 nm, and 638 nm

Augmented Reality and Its Application

Augmented Reality (AR) is a discipline that includes the interactive experience of a real-world environment, in which real-world objects and elements are enhanced using computer perceptual information. It has many potential applications in education, medicine, and engineering, among other fields. This book explores these potential uses, presenting case studies and investigations of AR for vocational training, emergency response, interior design, architecture, and much more

Computer-Generated Holography for Areal Additive Manufacture

With a market of approximately $10B, additive manufacture (AM) is an exciting next-generation technology with the promise of significant environmental and societal impact. AM promises to help reduce emissions and waste during manufacture while improving sustainability. Widely used in applications from hip implants to jet engines, AM remains the domain of experts due to the material and thermal challenges encountered. AM in metals is dominated by Laser Powder Based Fusion (L-PBF). Powder is spread in layers 10s of microns thick and selectively melted by scanning a small laser spot heat source over the bed. Traditional AM systems have limited ability to manage or compensate for heat generated. The rapidly moving heat source spot results in high thermal cycling and is a major influence on residual stress and distortion. Mechanical limitations in the galvoscanner mean that over or under-heating is common and can lead to voids, boiling and spatter. The scale difference between the part size and the spot size means that predictive modelling is beyond the scope of even today’s best computing clusters. These factors have led to frequent inability to ensure part quality without physical prototyping and destructive testing. This thesis sets out initial research into creating a radically new AM process that uses computer-generated holography (CGH) to produce complex light patterns in a single pulse. Projecting power to the whole layer at once will mean that the thermal properties of the powders before and after writing can be factored into the processed hologram and part design. It will also significantly reduce thermal gradients and melt-pool instability. The fields of additive manufacture and computer-generated holography are introduced in Chapter 1. Chapters 2 and 3 then provide more detail on CGH and AM modelling respectively. The first deliverable, a reusable software package capable of generating holograms, is presented in Chapter 4. Algorithms developed for the project are introduced in Chapter 4.3. The first project demonstrator, an AM machine capable of printing in resins using holographic projection is discussed in Section 6.2. This shows performance comparable to modern 3D printing machines and highlights the applicability of computer-generated holography to areal processes. Section 6.3 then discusses the ongoing development of a metal powder demonstrator. As this PhD forms the first stage of a larger project, only preliminary work on the powder demonstrator is discussed. Chapter 7 then draws conclusions and outlines the way forward for future research. The thesis appendices then discuss an in-depth discussion of algorithm performances in Appendices A and B. Appendices C and D then discuss digressions into the implementation. Appendices E and F present a laser induced damage threshold (LIDT) measurement system developed. Finally, Appendices G and H provide more detail on the software developed and Appendix I gives links to additional project resources.EP/T008369/1; EP/L016567/1; EP/V055003/